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British researchers at AllenAI have been actively refining their MolmoAct2 model, a 5B vision-language-action model designed for robot control. They’ve published several fine-tuned versions of this model on different robotics datasets.
- General Robotics Tasks: The team has released the MolmoAct2-LIBERO model, suitable for handling various general robotic tasks.
- Interactive Robot Control: They’ve also made available the MolmoAct2-DROID model, which excels in managing more complex interactive tasks between robots and humans.
- Precision Control: AllenAI has introduced MolmoAct2-BimanualYAM for precise joint-pose control of bimanual robots. Another variant, MolmoAct2-SO100_101, offers similar capabilities but with a different focus or dataset.
The key aspect here is the model’s openness—AllenAI has made their models fully accessible through open-source channels, including the sharing of training datasets and software. This approach not only accelerates research progress by allowing others to build upon these foundations but also promotes transparency in AI development. For anyone working on robot control via LLM inference, MolmoAct2 is now a valuable resource.
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### Takeaways
– AllenAI has iterated significantly on their MolmoAct2 model for various robotic tasks.
– The models are fully open-source, providing access to training datasets and software for broader research collaboration.
– This approach fosters transparency in AI development and accelerates the progress of robot control applications.
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